White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/104077/
Version: Accepted Version
Article:
Hoppe, CD, Cade, JE orcid.org/0000-0003-3421-0121 and Carter, M (2017) An evaluation of diabetes targeted apps for Android smartphone in relation to behaviour change
techniques. Journal of Human Nutrition and Dietetics, 30 (3). pp. 326-338. ISSN 0952-3871
https://doi.org/10.1111/jhn.12424
© 2016 The British Dietetic Association Ltd. This is the peer reviewed version of the following article: Hoppe C.D., Cade J.E. & Carter M. (2016) An evaluation of diabetes targeted apps for Android smartphone in relation to behaviour change techniques. J Hum Nutr Diet. doi:10.1111/jhn.12424; which has been published in final form at
https://dx.doi.org/10.1111/jhn.12424. This article may be used for non-commercial purposes in accordance with the Wiley Terms and Conditions for Self-Archiving.
[email protected] https://eprints.whiterose.ac.uk/
Reuse
Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website.
Takedown
If you consider content in White Rose Research Online to be in breach of UK law, please notify us by
Title: An evaluation of diabetes targeted apps for Android smartphone in relation to behaviour change
techniques
Authors:
Charlotte D Hoppe
School of Food Science and Nutrition
University of Leeds
Leeds LS2 9JT
and
Department of Nutrition and Dietetics
King’s College London
Franklin-Wilkins Building
Stamford Street
London SE1 9NH
Janet E Cade
Nutritional Epidemiology Group
School of Food Science and Nutrition
University of Leeds
Leeds LS2 9JT
Phone number: 0113 343 6946
Fax number: 0113 343 2982
E-mail address: [email protected]
Michelle Carter
Nutritional Epidemiology Group
School of Food Science and Nutrition
University of Leeds
Leeds LS2 9JT
Keywords: diabetes, behaviour change techniques, mobile apps, Smartphone
Details of role in the study: JEC had the initial idea of the study and had a major role in its design
Page 2 of 14
drafting the paper and MC contributed to the draft of the paper, reviewed its content and approved
the final version for submission.
Abstract
1
BACKGROUND: Mobile applications (apps) could support diabetes management through
2
dietary, weight and blood glucose self-monitoring; and promoting behaviour change. This study 3
aimed to evaluate diabetes apps for content, functions and behaviour change techniques (BCTs). 4
METHODS: Diabetes self-management apps for Android smartphones were searched for on
5
Google Play Store. Ten apps each from the following search terms were included; ‘diabetes’, 6
‘diabetes type 1’, ‘diabetes type 2’, ‘gestational diabetes’. Apps were evaluated by being scored 7
according to their number of functions and BCTs, price and user rating. 8
RESULTS: The average number of functions was 8.9 (SD 5.9) out of a possible maximum of
9
27. Furthermore, the average number of BCTs was 4.4 (SD 2.6) out of a possible maximum of 10
26. Apps with optimum BCT had significantly more functions (13.8, 95% CI 11.9, 15.9) than 11
apps that did not (4.7, 95% CI 3.2, 6.2; p<0.01) and significantly more BCTs (5.8, 95% CI 4.8, 12
7.0) than apps without (3.1, 95% CI 2.2, 4.1; p<0.01). Additionally, apps with optimum BCT 13
also cost more than other apps. In the adjusted models, highly rated apps had an average of 4.8 14
(95% CI 0.9, 8.7; p=0.02) more functions than lower rated apps. 15
CONCLUSION: ‘Diabetes apps’ include few functions or BCTs compared to the maximum 16
score possible. Apps with optimum BCTs could indicate higher quality. App developers should 17
consider including both specific functions and BCTs in ‘diabetes apps’ to make them more 18
helpful. More research is needed to understand components of an effective app for people with 19
diabetes. 20
Introduction
21
Diabetes mellitus is becoming increasingly prevalent worldwide. Currently, 387 million people are 22
diagnosed with diabetes, representing 8.3% of the global population(1), and this figure is expected to 23
rise to 592 million by the year 2035. Affected individuals have to manage it for the rest of their lives. 24
A number of long term complications are associated with diabetes(2), and effective control of blood 25
pressure and blood glucose reduces the risk of both macro-vascular and micro-vascular diseases(3; 4; 26
5). It is therefore important to carefully manage the disease to minimise its impact on morbidity and
27
mortality. 28
Page 3 of 14
In 2015, 76% of the UK population owned a Smartphone(6), and it is predicted that by 2017, 2.5
30
billion people worldwide will own a Smartphone (7). Smartphones therefore have the potential to be
31
used to manage disease using “mHealth” (mobile health) applications(8). There were over 6,000 32
medical apps available on the Android market in 2013(9), and this has since nearly quadrupled to 33
23,000 apps(10). Many apps aim to support self-management for people with diabetes, however, while 34
mHealth apps may benefit people suffering from chronic disease, there are also problems associated 35
with them. These problems include lack of evidence on clinical effectiveness, lack of integration into 36
the health care system and potential threats to safety (9). A recent study found that health apps in the 37
UK NHS Health Apps Library had poor compliance with data protection principles(11). For an app to 38
be recommended to patients by health professionals, its effectiveness should be scientifically proven. 39
Most apps do not have a strong evidence base demonstrating their effectiveness. The US Food and 40
Drug Administration (FDA) defined a mobile app to be a medical device if it was intended to 41
diagnose, cure, mitigate, treat or prevent a disease(12), needing FDA approval before being released 42
to the market. Unapproved apps could lead to adverse health effects if users substituted a doctor’s
43
visit with consulting an app(9). 44
45
There is substantial research investigating new technology in the use of managing disease. However, 46
in relation to diabetes-linked conditions, these are mostly focused on weight loss, and look at web-47
based programmes rather than mobile apps(13; 14). Additionally, these studies have not looked at BCTs,
48
but rather measure BMI (Body Mass Index, kg/m2) or body weight as outcomes. While these are
49
appropriate outcomes to measure effectiveness of diabetes management interventions, it is also 50
important to understand which BCTs are promoting effective behaviour change. Some diabetes 51
management apps have been evaluated, but these were web-based rather than mobile app-based(16; 17), 52
and measure user satisfaction or usability(18; 19) rather than BCTs. A qualitative study on usability of 53
apps for weight loss(20) concluded that app designers should employ BCTs to improve effectiveness. 54
Furthermore, a Cochrane review(21) investigated which computer-based intervention would be most 55
effective at improving HbA1c levels in adults with diabetes, and found that mobile apps were more 56
effective than computer programmes used in hospitals or at home. The authors thought that this was 57
due to the inclusion of control theory techniques such as self-regulation. 58
59
Twenty six distinct, theory-linked BCTs have been described and tested(22). BCTs are theory-based 60
methods to facilitate change in individuals, and examples include ‘Prompt intention formation’ and 61
‘Model or demonstrate behaviour’ which could be incorporated into mobile apps. A meta-analysis(23) 62
Page 4 of 14
healthy eating. It found that interventions that combined self-monitoring with at least one other 64
technique derived from control theory were significantly more effective than the other interventions. 65
The aim of this study was to evaluate Android apps for people with diabetes in terms of which 66
functions they included and which BCTs they employed to encourage behaviour change. To our 67
knowledge there is no research assessing the inclusion of BCTs in interventions used in diabetes 68
mobile apps. This research could provide a basis for improving ‘diabetes apps’ in the future.
69
Methods
70
App selection
71
Google Play Store (UK) for Android was used as a database to search for relevant apps on 27 October 72
2014. Since there is no existing appropriate category, these specific search terms were used: 73
‘diabetes’, ‘diabetes type 1’, ‘diabetes type 2’ and ‘gestational diabetes’. The apps were initially pre-74
screened for suitability before being downloaded. Inclusion criteria were 1) to be intended for patients 75
with type 1, 2 or gestational diabetes, 2) to be addressing any aspect of management of diabetes (e.g. 76
blood glucose monitoring, medication, healthy diet), 3) to have stand-alone functionality (i.e. not 77
requiring membership in a specific programme or website to function) and 4) to be in English. The 78
exclusion criteria were 1) to be for self-diagnosis for the user and 2) to be intended for education of 79
medical personnel. Apps that did not function properly on the test phone, for example, they would 80
not open or we could not get past the introduction screen, were also excluded. This pre-screening was 81
based on the app descriptions and screenshots provided in Google Play Store. The number of medical 82
apps available on Google Play Store is 23,000(10) with only a small proportion of these of relevance
83
to people with diabetes . The exact number of ‘diabetes apps’ could not be determined as Google Play
84
Store does not state the number of search results. Each search only shows 200 app results. Due to 85
restraints in time and resources, the number of apps included had to be restricted. The first ten apps 86
passing the pre-screening from each search term were included, giving 40 apps in total. App ranking 87
is partly determined by App Store Optimisation, which among other aspects takes into account 88
keyword alignment (i.e. how the user’s search term matches with words in the app title and 89
description), and app performance (e.g. app ratings and number of downloads)(24). An algorithm is 90
used to determine the exact ranking, and this is not available to the general public, and undergoes 91
continuous change(25). For the purposes of this study it is therefore not possible to find out the total 92
number and ranking of all available diabetes-related apps. 93
94
Following identification, the apps were downloaded and evaluated again based on the same inclusion 95
and exclusion criteria as stated above. At this point some of the apps were excluded, and therefore a 96
Page 5 of 14
ten from each search term (Figure 1). This second search was performed on 9 June 2015. Five apps 98
were independently evaluated by another assessor in order to determine the repeatability and relative 99
validity of the assessments. 100
101
App testing
102
Each app that met the inclusion and exclusion criteria was used by the author (CH) to identify the 103
functions and BCTs included. The results were recorded in a data extraction form (Table 4) recording 104
the functions and BCTs included in each app. A possible 27 functions were categorised into 105
‘Provision of information’, ‘Allows self-recording’, ‘Generates output from self-recording’, ‘Data 106
management’ and ‘Other’. The 26 BCTs identified by Michie and Abraham(22), were categorised into 107
‘Motivation enhancing’, ‘Planning and preparation’ and ‘Goal striving and persistence’ (see a list of 108
these in Figures 2-3). Therefore, a maximum score of 53 could be obtained by each app. Each app 109
was downloaded immediately before assessment using the author’s private mobile phone. The 110
majority of apps were evaluated between the 3 November and 10 December 2014, and apps identified 111
in the second search stage were evaluated between the 9 June and 15 June 2015. Some apps had data 112
collection functions, such as recording blood glucose readings or food intake, and where this was the 113
case, they were used for two days to give sufficient data for graph generation. Apps which did not 114
have data collection functions were explored to extract information on all other functions and BCTs 115
present. 116
Based on the meta-analysis by Michie et al. found (23), the most effective combination of BCTs is
117
‘Prompt self-monitoring of behaviour’ in combination with at least one of four other self-regulatory 118
techniques: ‘Prompt intention formation’, ‘Prompt specific goal setting’, ‘Provide feedback on
119
performance’ and ‘Prompt review of behavioural goals’. This was evaluated as ‘optimum BCT’ in 120
this study’. 121
122
Statistical analysis
123
The results were analysed using the statistical software Stata/IC (Release 13.1; Stata Corp, College 124
Station, TX). T-tests were performed to assess the difference in mean number of functions, number 125
of BCTs, overall score, price and user rating according to inclusion of ‘optimum BCT’, price (free or 126
paid) and user rating. For the latter, user rating, normally ranging from one to five, was divided into 127
the following two groups; low=1.0-4.0 and high=4.1-5.0. The uneven division of user rating was due 128
to average app rating for the majority of apps being greater than 4. Regression was performed to see 129
Page 6 of 14
price (£) and user rating. Regression models for price adjusted for user rating and vice versa. Cohen’s 131
kappa was calculated to determine the inter-rater reliability from the duplicate extracted data. 132
133
Results
134
App selection
135
The initial pre-screening gave a list of 40 apps to be further evaluated for eligibility. Of these, 13 apps 136
were excluded due to non-conformity with inclusion criteria (Figure 1). The excluded apps were 137
either intended for training of health personnel (n=2), no longer available at the point of download 138
(n=5), required the use of a website along with the app (n=2), non-functional (n=1), not in English 139
(n=1) or not for previously diagnosed patients (n=2). This initially gave 27 apps to be included in the 140
study. However, to improve the generalisability of the study, 13 further apps were added from a 141
repeated search to give 40 apps in total. These were individually pre-screened before inclusion. 142
143
App testing
144
Based on overall score (i.e. the sum of number of functions and BCTs), Diabetes Tracker by Mig 145
Super, Diabetes:M by Rossen Varbanov and Diabetes Companion by mySugr GmbH ranked highest, 146
scoring 29, 27 and 26 out of 53 respectively. These were all apps that offered recording of various 147
physical measures, e.g. blood glucose, weight and height. They all included ‘optimum BCT’. The
148
apps scoring lowest overall were Type 1 Diabetes by Colby Taylor, Recipes for Diabetes by 149
University of Illinois Extension and Diabetic Diet Samples by Awesomeappcenter LLC, with scores 150
of 2, 3 and 4 and out of 53 respectively. These apps focused on giving information and advice about 151
the disease and how to manage it. The average overall score was 13.2 (standard deviation (SD) 7.4) 152
out of 53 (Table 3). 153
154
The average number of functions included in the apps was 8.9 (SD 5.9) out of 27 (Table 3). 155
The most common functions were ‘Enter blood glucose values’ and ‘Export data to Smartphone/send
156
data’, which were included in 23 and 22 of the apps respectively. This involved downloading data or
157
graphs to the Smartphone directly; sending it to a specified email address; or uploading it to a cloud 158
based storage system. Other common functions included enter medication; weight; carbohydrates 159
consumed. Thirty-two out of the 40 apps included ‘Any other (describe)’, a mixed group of functions 160
including anything that was not included in the rest of the list. These ranged from offering a forum to 161
communicate with other people with diabetes; a game including a point system for doing beneficial 162
activity; making a shopping list for meals; and information on which McDonald’s meals were
163
Page 7 of 14
generate a table of nutrients consumed. None of the apps included ‘Technological additional feature:
165
Connect glucose meter to Smartphone to transfer data’ (Figure 2).
166
167
The inclusion of BCTs in apps was far less common than the inclusion of functions. The average 168
number of BCTs was 4.4 (SD 2.7) out of 26 (Table 3). The most commonly included technique was 169
‘Prompt self-monitoring of behaviour’ (n=23) and ‘Prompt intention formation’ (n=20). These 170
techniques are both among the self-regulatory techniques which were identified as most effective 171
when used in combination with each other(23). However, fewer apps (n=18) had ‘optimum BCT’ 172
defined as ‘Prompt self-monitoring of behaviour’ with at least one other self-regulatory technique 173
(i.e. ‘optimum BCT’ ‘Prompt intention formation’, ‘Prompt specific goal setting’, ‘Provide feedback
174
on performance’ and ‘Prompt review of behavioural goals’). Five BCTs were not used in any of the 175
apps: ‘Prompt barrier identification’, ‘Agree on behavioural contract’, ‘Prompt practice’, ‘Prompt
176
self-talk’, and ‘Motivational interviewing’ (Figure 3).
177
178
App characteristics
179
Apps including ‘optimum BCT’ had more functions (13.8, 95% CI 11.9, 15.9) than apps that did not 180
(4.7, 95% CI 3.2, 6.2; p<0.01). This was also true in all the subgroups of functions. The same was 181
found to be true with regard to the BCTs themselves, with more BCTs (5.8, 95% CI 4.8, 7.0) in apps 182
with ‘optimum BCT’ than in apps without (3.1, 95% CI 2.2, 4.1; p<0.01). Logically, apps with
183
‘optimum BCT’ also had an overall higher score (19.8, 95% CI 17.1, 22.5) than those that did not 184
have ‘optimum BCT’ (7.9, 95% CI 6.3, 9.4; p<0.01). Furthermore, apps with ‘optimum BCT’ had a
185
higher price (in £) (3.2, 95% CI 0.6, 5.9) than those without (0.3, 95% CI -0.0, 0.5; p=0.01) (Table 186
1).
187
188
Apps with a high user rating had more functions (10.6, 95% CI 8.3, 13.9) than those that had a low 189
rating (6.2, 3.0, 9.5; p=0.03). This was also true for the functions subgroups, except ‘Other’. 190
Conversely, the number of BCTs included was not related to user rating (high user rating number of 191
BCTs 4.5, 95% CI 2.8, 6.1) vs. (low user rating number of BCTs 4.5, 95% CI 3.6, 5.3; p=0.98). Only 192
BCTs in the subgroup ‘Goal striving and persistence’ were significantly more common in highly rated
193
apps (2.7, 95% CI 2.0, 3.4) compared to low user rated apps (1.5, 95% CI 0.4, 2.6; p=0.05). However, 194
there was an indication of a higher user rating in apps with ‘optimum BCT’ (4.4, 95% CI 4.2, 4.5) 195
than in those without (4.0, 95% CI 3.6, 4.4; p=0.07) (Table 1). 196
Page 8 of 14
The regression analysis also resulted in a significant association between number of functions, but 198
not BCTs, and user rating (Table 2). In the adjusted models, highly rated apps had an average of 4.8 199
(95% CI 0.9, 8.7; p=0.02) more functions than lower rated apps. However, payment for an app was 200
significantly related to higher number of BCTs; paid apps had a higher number of BCTs by 1.9 (95% 201
CI 0.1, 3.8; p=0.04) than free ones. Price did not affect the overall score, but user rating was associated 202
with overall score. Highly rated apps had a higher overall score by 5.1 (95% CI 0.1, 10.0; p=0.04). 203
204
The inter-rater reliability gave an average agreement of 86% and kappa was 0.68, corresponding to a 205
substantial or good agreement between raters. 206
207
Discussion
208
The inclusion of ‘optimum BCT’ has been used as a proxy for app quality, because this combination
209
of BCTs is most effective at changing behaviour(23) and is therefore potentially most beneficial to a 210
person with diabetes using the app. The analysis showed that both the number of functions and the 211
number of BCTs included in the apps were quite low. The average number of BCTs was only 4.4 (SD 212
2.6) out of 26. Therefore, BCTs were probably not actively considered in the development of the 213
apps. Diabetes is a chronic disease requiring lifelong management; changing behaviour is key to 214
achieving this successfully(26). The combination of BCTs that was found to be most effective(23), was
215
only included in 18 of the 40 apps. It is clear that there is still considerable potential for improvement 216
of BCT inclusion in ‘diabetes apps’.
217
218
Apps with optimum BCT had significantly more functions and BCTs, indicating that these could be 219
predictors of app quality. Furthermore, user rating significantly predicted the number of functions 220
included; whereas price was linked to increased number of BCTs. There was a non-statistically 221
significant suggestion of a higher user rating in apps with ‘optimum BCT’ compared to apps without 222
the optimum combination of BCTs. The validity of user rating as a predictor of app effectiveness is 223
uncertain, as most users are unlikely to base their rating on whether they managed to change 224
behaviour. Research on user reviews(27) found that the most common causes of complaint were among 225
others attractiveness, stability and compatibility. None of the causes listed were related to the apps’ 226
ability to change behaviour. Apps with ‘Optimum BCT’ cost more than others. West et al.(29), who 227
appraised a number of apps based on their potential to influence behaviour change found that more 228
expensive apps were more likely to be scored as intending to promote health or prevent disease. 229
Page 9 of 14
The small sample size of the study, only 40 apps were evaluated, could have limited our ability to 231
determine predictors of app quality. With approximately 23,000(10) health apps available, the total
232
number of ‘diabetes apps’ is likely to be much greater than 40 and the sample size therefore presents
233
a limitation to this study. Additionally, iTunes Store was not searched for apps, and there is a 234
possibility that there are some key diabetes management apps which were therefore missed. We did 235
however undertake independent evaluation of a subsample of the apps included and found good 236
agreement between reviewers. Resource implications precluded duplicate extraction of all apps, 237
which is another limitation of this study. 238
239
Diabetes Tracker by Mig Super, which scored highest in this study, is an app that includes recording 240
of blood glucose, carbohydrate consumption and activity, as well as providing tips for recipes and 241
physical exercises, dietary guidelines for each type of diabetes and information on so-called 242
‘superfoods’. The app that scored lowest, Type 1 Diabetes by Colby Taylor included different types 243
of functions. They were more informative and advisory; giving rather limited information about the 244
condition and about healthy meals that could keep blood sugar levels stable. It is clear that apps 245
directed at people with diabetes include a range of different functions, making comparisons between 246
them challenging. This variation in intended use creates a heterogeneity which might impact on the 247
results. 248
249
As previously mentioned, there were five BCTs that were not included in any app. It might be 250
unrealistic to think that all of the BCTs can feasibly be fitted into a mobile phone app. Some 251
techniques would be more challenging to include since there was no link to a human decision maker, 252
e.g. deciding when the target behaviour has been reached, or if the participant has relapsed. Peer or 253
health care professional support would be possible through links to social media or downloads to 254
surgery records. ‘Agree on behavioural contract’ could have been included in an app, for instance as
255
behavioural goals written by the user themselves within the app or for the user to agree to pre-written 256
goals. 257
258
The function ‘Connect glucose meter to Smartphone to transfer data’ was not included in any apps.
259
The list of possible functions was developed by the author, partly based on similar research done by 260
Chen(30) as well as knowledge about which elements are important when managing diabetes. 261
However, expecting the inclusion of this function is not unreasonable. There are ‘diabetes apps’ 262
currently on the market, not identified by our search which do have the possibility of being connected 263
Page 10 of 14
Diabetes) or wirelessly via Bluetooth (e.g. iHealth Gluco-Smart by iHealth Lab Inc.), and thereby 265
transferring glucose readings directly to the diabetes management app. This is a great advantage to 266
the user because it eliminates the burden of manually entering blood glucose values into the app. 267
268
As briefly mentioned previously, one app included a game where the user could earn points for 269
undertaking health behaviours (Diabetes Companion by mySugr GmbH). Gamification is a term 270
describing the use of game elements in a non-game setting(31). There is some evidence that 271
gamification is useful in the management of diabetes(31; 32), and Diabetes Companion is also one of 272
the highest scoring apps in this study, possibly due to greater facilitation of some BCTs. Similarly, 273
social support has repeatedly been shown to have a beneficial effect on diabetes management(33),(34), 274
but only nine out of the 40 apps provided at forum for the users to communicate among each other 275
(‘Link to social media’). Again, this aspect could be worth including in a ‘diabetes app’ in order to
276
improve outcomes for the user. 277
A weakness of this study is that it did not measure actual behaviour change as an outcome. Instead, 278
the inclusion of specific BCTs was used as a proxy for effectiveness(23). The optimum BCT score was 279
derived from a peer reviewed meta-analysis including 122 papers. Although this was not focussed on 280
diabetes management, but on diet and physical activity, these are both factors important in the 281
management of type 2 diabetes. More recent evidence, published after the main part of the present 282
study was conducted is conflicting. Avery et al. conducted a meta-analysis to determine which BCTs 283
were most effective at increasing levels of physical activity, and consequently improving HbA1c 284
levels in adults with diabetes type 2(35). The four most effective techniques they found were ‘Prompt 285
focus on past success’, ‘Barrier identification/problem-solving’, ‘Use of follow-up prompts’ and 286
‘Provide information on whereand when to perform physical activity’. ‘Prompt focus on past success
287
could be perceived as included within ‘self-monitoring of behaviour’ provided that this behaviour 288
was indeed a success. Apart from that, the techniques found to be most effective differed completely. 289
This suggests that finding BCTs that can be generalised to behaviour change interventions is difficult 290
and may be behaviour or condition specific. Future work may include different interpretations of the 291
most effective BCTs or undertaking a randomised controlled trial of apps including measurement of 292
behaviour change as an outcome. 293
294
The aim of this study was to evaluate ‘diabetes apps’ with regard to behaviour change techniques.
295
The same taxonomy of BCTs has previously been used in relation to mobile apps for physical activity 296
and diet(36). However, we believe this is the first study looking at BCTs in ‘diabetes apps’. The mobile 297
Page 11 of 14
been approved by a professional body may be problematic if users are not instructed correctly. The 299
European Directory of Health Apps (2012) reviewed about 200 health apps in cooperation with 300
patient groups(38). The ‘diabetes apps’ included that overlapped with the apps evaluated here were 301
Carbs & Cals by Chello Publishing, Diabetes UK Tracker by Diabetes UK, Glucose Buddy by 302
Azumio, Inc. and OnTrack Diabetes by Medivo. The Directory did not quantitatively evaluate the 303
apps; included apps were recommended by patient groups. These four apps ranked within the upper 304
half of the apps evaluated in the present study. Demidowich et al. assessed 42 ‘diabetes apps’ in
305
2011(19), though they did not include BCTs. Their highest ranking apps were Glucool Diabetes, 306
OnTrack Diabetes, Dbees and Track3 Diabetes Planner. This agrees with the results from the present 307
study which also evaluated Glucool Diabetes by 3qubits and OnTrack Diabetes by Medivo, ranking 308
them seventh and eighth overall. 309
310
In conclusion, we have conducted a study evaluating diabetes self-management apps with regard to 311
BCTs. This is highly relevant in today’s society as both Smartphone usage and diabetes is becoming 312
increasingly prevalent. Behaviour change is an essential aspect of successful diabetes management, 313
and incorporating BCTs in ‘diabetes apps’ is a great opportunity to provide people with diabetes with
314
a self-management tool. However, the ‘diabetes apps’ on the Android market were found to generally 315
include few functions and even fewer BCTs. The three apps scoring most highly in this study were 316
Diabetes Tracker by Mig Super, Diabetes:M by Rossen Varbanov and Diabetes Companion by 317
mySugr GmbH, these had the most functions and BCTs and including the combination of BCTs 318
thought to be most effective at changing behaviour. Health professionals may want to recommend 319
these apps to people with diabetes. More research on the effectiveness of BCTs in mobile apps is 320
needed, this time with more tangible outcomes of behaviour change techniques, for instance HbA1c 321
levels or weight change. With effectiveness established, app developers could work in conjunction 322
with doctors, dietitians and psychologists, who have expert knowledge in the field, to include more 323
Acknowledgements
326
The authors acknowledge the support from Marion Hery in the duplicate evaluation of the apps. 327
328
Conflict of interest
329
This work was carried out without external funding and no competing financial interests exist. JEC 330
and MC have developed a smartphone app My Meal Mate which aims to support weight loss. 331
References
332
1. International Diabetes Federation (2014) IDF DIABETES ATLAS.
333
2. van Dieren S, Beulens JWJ, van der Schouw YT et al. (2010) The global burden of diabetes and its complications: an emerging pandemic.
334
European Journal of Cardiovascular Prevention & Rehabilitation 17, s3-s8.
335
3. Gaede P, Lund-Andersen H, Parving HH et al. (2008) Effect of a multifactorial intervention on mortality in type 2 diabetes. New England Journal
336
of Medicine 358, 580-591.
337
4. Lyakishev AA (2006) Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. Results of the DCCT/EDIC study.
338
Kardiologiya 46, 73-73.
339
5. AA (1998) Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. British Medical
340
Journal 317, 703-713.
341
6. Deloitte (2015) Mobile Consumer 2015: The UK cut. http://www.deloitte.co.uk/mobileuk/
342
7. EMarketer (2014) 2 Billion Consumers Worldwide to Get Smart(phones).
343
8. World Health Organisation (2011) mHealth: New horizons for health through mobile technologies.
344
9. Eng DS, Lee JM (2013) The Promise and Peril of Mobile Health Applications for Diabetes and Endocrinology. Pediatr Diabetes 14, 231-238.
345
10. AppBrain (2015) Most popular Google Play categories.
346
11. Huckvale K, Prieto JT, Tilney M et al. (2015) Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic
347
assessment. BMC Medicine 13, 1-13.
348
12. US Food and Drug Administration (2015) Mobile Medical Applications - Guidance for Industry and Food and Drug Administration Staff.
349
13. Harvey-Berino J, Pintauro S, Buzzell P et al. (2002) Does using the Internet facilitate the maintenance of weight loss? International Journal of
350
Obesity 26, 1254-1260.
351
14. Aguilar-Martínez A, Solé-Sedeño JM, Mancebo-Moreno G et al. (2014) Use of mobile phones as a tool for weight loss: a systematic review.
352
Journal of Telemedicine and Telecare 20, 339-349.
353
15. Jeffery RW, Epstein LH, Wilson GT et al. (2000) Long-term maintenance of weight loss: Current status. Health Psychology 19, 5-16.
354
16. McKay H, Feil, E., Glasgow, R. & Brown, J. (1998) Feasibility and Use of an Internet Support Service for Diabetes Self-Management. The
355
Diabetes Educator 24, 174-179.
356
17. Balas EA, Krishna S, Kretschmer RA et al. (2004) Computerized knowledge management in diabetes care. Medical Care 42, 610-621.
357
18. Bain TM, Jones ML, O’Brian CA et al. (2015) Feasibility of smartphone-delivered diabetes self-management education and training in an
358
underserved urban population of adults. Journal of Telemedicine and Telecare 21, 58-60.
359
19. Demidowich AP, Lu K, Tamler R et al. (2012) An evaluation of diabetes self-management applications for Android smartphones. Journal of
360
Telemedicine and Telecare 18, 235-238.
361
20. Tang J, Abraham C, Stamp E et al. (2015) How can weight-loss app designers' best engage and support users? A qualitative investigation. British
362
Journal of Health Psychology 20, 151-171.
363
21. Pal K ES, Michie S, Farmer AJ, Barnard ML, Peacock R, Wood B, Inniss JD, Murray E. (2013) Computer-based diabetes self-management
364
interventions for adults with type 2 diabetes mellitus. Cochrane Database of Systematic Reviews.
365
22. Abraham C, Michie S (2008) A taxonomy of behavior change techniques used in interventions. Health Psychology 27, 379-387.
366
23. Michie S, Abraham C, Whittington C et al. (2009) Effective techniques in healthy eating and physical activity interventions: A meta-regression.
367
Health Psychology 28, 690-701.
368
24. Briggs J (2015) Everything You Need to Know About Mobile App Search. In Moz, vol. 2015.
369
25. Gauchet S (2013) OPTIMIZE YOUR GOOGLE PLAY STORE APP DETAILS PAGE. In apptamin.
370
26. Peyrot M, Rubin RR (2007) Behavioral and Psychosocial Interventions in Diabetes: A conceptual review. Diabetes Care 30, 2433-2440.
371
27. Fu B, Lin J, Li L et al. (2013) Why people hate your app: making sense of user feedback in a mobile app store. In Proceedings of the 19th ACM
372
SIGKDD international conference on Knowledge discovery and data mining, pp. 1276-1284. Chicago, Illinois, USA: ACM.
373
28. Pagano D, Maalej W (2013) User feedback in the appstore: An empirical study. In Requirements Engineering Conference (RE), 2013 21st IEEE
374
International, pp. 125-134.
375
29. West JH HP, Hanson CL, Barnes MD, Giraud-Carrier C, Barrett J (2012) There’s an App for That: Content Analysis of Paid Health and Fitness
376
Apps. J Med Internet Res 14, e72.
377
30. Chen J CJ, Allman-Farinelli M (2015) The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment. JMIR mHealth uHealth 3,
378
e104.
379
31. Cugelman B (2013) Gamification: What It Is and Why It Matters to Digital Health Behavior Change Developers. JMIR Serious Games 1, e3.
380
32. Kamel Boulos MN, Gammon, S., Dixon, M. C., MacRury, S. M., Fergusson, M. J., Miranda Rodrigues, F., Mourinho Baptista, T., Yang, S. P.
381
(2015) Digital Games for Type 1 and Type 2 Diabetes: Underpinning Theory With Three Illustrative Examples. JMIR Serious Games 3, e3.
382
33. Toma T, Athanasiou T, Harling L et al. (2014) Online social networking services in the management of patients with diabetes mellitus:
383
Systematic review and meta-analysis of randomised controlled trials. Diabetes Research and Clinical Practice 106, 200-211.
384
34. van Dam HA, van der Horst FG, Knoops L et al. (2005) Social support in diabetes: a systematic review of controlled intervention studies. Patient
385
Education and Counseling 59, 1-12.
386
35. Avery L, Flynn D, Dombrowski SU et al. (2015) Successful behavioural strategies to increase physical activity and improve glucose control in
387
adults with Type 2 diabetes. Diabetic Medicine, n/a-n/a.
388
36. Brannon EE, Cushing CC (2014) Is There an App for That? Translational Science of Pediatric Behavior Change for Physical Activity and Dietary
389
Interventions: A Systematic Review. Journal of Pediatric Psychology.
Page 13 of 14
38. European Commission (2012-2013) European Directory of Health Apps 2012-2013: A review by patient groups and empowered consumers. London: PatientView.
393
394
The mobile applications
395
1. 3qubits. 2013. Glucool Diabetes (Premium) (1.4.3.1). [mobile app]. [Date accessed: 396
06.12.2014] 397
2. Apps Den. 2014. Gestational Diabetes (1.0). [mobile app]. [Date accessed: 07.12.2014] 398
3. Avinash Kulkarni. 2012. Diabetes Guide (1.0). [mobile app]. [Date accessed: 10.12.2014] 399
4. Awesomeappcenter LLC. 2013. Diabetic Diet Samples (1.0). [mobile app]. [Date accessed:
400
26.11.2014] 401
5. Azumio, Inc. 2012. Glucose Buddy: Diabetes Log (1.0). [mobile app]. [Date accessed: 402
06.11.2014] 403
6. Colby Taylor. 2012. Type 1 Diabetes (1.0). [mobile app]. [Date accessed: 19.11.2014] 404
7. Chello Publishing Limited. 2014. Carbs & Cals – Diabetes & Diet (2.11). [mobile app]. [Date 405
accessed: 12.06.2015] 406
8. David Froehlich. 2012. DiaLog: Diabetes Logbook (1.3.12). [mobile app]. [Date accessed: 407
10.06.2015] 408
9. Dheryta. 2013. T1DM – Manage Type 1 Diabetes (3.0). [mobile app]. [Date accessed: 409
20.11.2014] 410
10.Diabetes Digital Media Ltd. 2015. Diabetes PA (Diabetes Manager) (1.1.0). [mobile app]. 411
[Date accessed: 11.06.2015] 412
11.Diabetes Digital Media Ltd. 2014. Diabetes Forum (3.11.34). [mobile app]. [Date accessed: 413
26.11.2014] 414
12.Diabetes UK. 2013. Diabetes UK Tracker (1.4). [mobile app]. [Date accessed: 19.11.2014] 415
13.EasyMobileApp. 2014. Easy Diabetes (1.7.0). [mobile app]. [Date accessed: 26.11.2014] 416
14.Gordon Wong. 2015. BG Monitor Diabetes Pro (6.8.4). [mobile app]. [Date accessed: 417
12.06.2015] 418
15.Gtxcel. 2014. Diabetes Forecast® (4.41). [mobile app]. [Date accessed: 11.6.2015] 419
16.Heyworld.dk. 2014. Pregnant with diabetes (1.1.0). [mobile app]. [Date accessed: 420
10.12.2014] 421
17.Jeschua Schang. Diabetes Diary (1.2.2). [mobile app]. [Date accessed: 12.06.2015] 422
18.Klimaszewski Szymon. 2014. Diabetes – Glucose Diary (1.3.04). [mobile app]. [Date 423
accessed: 16.11.2014] 424
Page 14 of 14 26.11.2014]
427
21.Medhelp, Inc – Top Health Apps. 2014. Sugar Sense – Diabetes App (1.0.1). [mobile app]. 428
[Date accessed: 06.12.2014] 429
22.Medivo. 2014. OnTrack Diabetes (3.2.5). [mobile app]. [Date accessed: 03.11.2014] 430
23.Mig Super. 2014. Diabetes Tracker (1.8). [mobile app]. [Date accessed: 09.06.2015] 431
24.moveforward. 2012. Diabetes Forum for Diabetics (1.3.18). [mobile app]. [Date accessed: 432
15.06.2015] 433
25.mySugr GmbH. 2014. Diabetes Companion (2.3.4). [mobile app]. [Date accessed:
434
13.11.2014] 435
26.Naster Solomon. 2014. Gestational Diabetes (1.0). [mobile app]. [Date accessed: 08.12.2014] 436
27.NetSummitApps. 2015. Diabetic Recipes! (1.2). [mobile app]. [Date accessed: 12.06.2015] 437
28.Personal Remedies. 2014. Diabetes Type 2 (1.0). [mobile app]. [Date accessed: 20.11.2014] 438
29.Peter Wescott. 2011. Diabetic Assistant (2.0). [mobile app]. [Date accessed: 11.06.2015] 439
30.Riafy Technologies. 2014. Diabetes Recipes Free (7.0.0). [mobile app]. [Date accessed: 440
11.11.2014] 441
31.Rossen Varbanov. 2015. Diabetes:M (3.0.2). [mobile app]. [Date accessed: 09.06.2015] 442
32.Rossen Varbanov. 2014. My Diabetes (2.3.5). [mobile app]. [Date accessed: 12.11.2014] 443
33.SINIVO Ltd. & Co KG. 2014. SiDiary Diabetes Management (1.27). [mobile app]. [Date 444
accessed: 28.11.2014] 445
34.Social Diabetes. 2014. Social Diabetes (2.8.4). [mobile app]. [Date accessed: 03.11.2014] 446
35.SquareMed Software GmbH. 2014. Diabetes Connect (2.0.2). [mobile app]. [Date accessed: 447
19.11.2014] 448
36.SquareMed Software GmbH. 2014. Diabetes Plus (1.0.4). [mobile app]. [Date accessed: 449
16.11.2014] 450
37.Suderman Solutions. 2014. Diabetes Journal (1.4.4). [mobile app]. [Date accessed: 451
11.11.2014] 452
38.TopAppsFor-Health. 2014. Diabetes Tools – Glucose (1.2). [mobile app]. [Date accessed: 453
10.12.2014] 454
39.Twayesh Projects. 2014. AudioBook – Diabetes (26.0). [mobile app]. [Date accessed: 455
10.06.2015] 456
40. University of Illinois Extension. 2013. Recipes for Diabetes (1.1). [mobile app]. [Date 457
